1. ERDC Knowledge Core
  2. Engineer Research and Development Center (ERDC)
  3. Construction Engineering Research Laboratory (CERL) - (9/9/1968 - present)
  4. Publication
  5. Technical Report
Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/34163
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dc.contributor.authorBastian, Elizabeth G.-
dc.contributor.authorMyers, Natalie R. D.-
dc.contributor.authorEhlschlaeger, Charles R.-
dc.contributor.authorBurkhalter, Jeffrey A.-
dc.date.accessioned2019-09-23T19:32:07Z-
dc.date.available2019-09-23T19:32:07Z-
dc.date.issued2019-09-
dc.identifier.govdocERDC/CERL TR-19-14-
dc.identifier.urihttps://hdl.handle.net/11681/34163-
dc.identifier.urihttp://dx.doi.org/10.21079/11681/34163-
dc.descriptionTechnical Report-
dc.description.abstractTwitter has increasingly been used to study various research topics such as election predictions, disease spread, etc. However, social media platforms do not saturate the entire population in a study area, especially in emerging nations, only representing more affluent subpopulations. The U.S. Army Engineer Research and Development Center, Construction Engineering Research Laboratory (ERDC-CERL), as part of a project entitled Framework for the Integration of Complex Urban Systems (FICUS), is quantifying the utility of demographic information to inform neighbor-hood-scale social media models. Using the example topic of infrastructure, an open-source model was constructed to collect Twitter data from the metropolitan Philippines area of Manila, geotag tweets to neighborhood grid cells based on language analysis, and produce a sentiment topic map. ERDC’s social media analysis tools incorporate quantifiable uncertainties with specific on-the-ground reporting techniques. By using the Humanitarian Crisis (HC) framework developed by PACOM (another FICUS product) as a model, a framework quantifying the likelihood of being a regular social media user was created to implement a data-driven, bottom-up framework construction nested within a knowledge-based established framework. This framework, and any other produced by the FICUS team serve as case studies for augmenting the military operational environment with quantifiable reduced uncertainties.en_US
dc.description.sponsorshipUnited States. Office of the Assistant Secretary of the Army for Acquisition, Logistics, and Technology.en_US
dc.description.sponsorshipMilitary Facilities Engineering Technology Program (U.S.)-
dc.description.tableofcontentsAbstract ................................................................................................................................................... ii Figures and Tables ................................................................................................................................. iv Preface ..................................................................................................................................................... v 1 Introduction ..................................................................................................................................... 1 1.1 Background ..................................................................................................................... 1 1.2 Objective .......................................................................................................................... 3 1.3 Approach ......................................................................................................................... 3 2 FICUS Demographic Models ......................................................................................................... 4 2.1 Technology overview ....................................................................................................... 4 2.2 Initial humanitarian crisis framework ............................................................................ 7 2.3 Social media use in the Philippines ............................................................................... 9 3 Developing the FICUS Twitter Tool ............................................................................................. 11 3.1 Collection ....................................................................................................................... 12 3.2 Geotagging based on language modeling ................................................................... 13 3.2.1 Geotagging methodology functions .................................................................................. 14 3.2.2 Model validity testing ......................................................................................................... 16 3.3 Sentiment analysis ....................................................................................................... 18 3.4 Visualization .................................................................................................................. 18 4 Developing the Social Media Use Framework .......................................................................... 20 4.1 Input data ...................................................................................................................... 21 4.2 Risk value ...................................................................................................................... 21 4.3 Weights .......................................................................................................................... 23 4.4 Unknown components .................................................................................................. 23 4.5 Roll-up computation ..................................................................................................... 23 4.5.1 Favorability functions ........................................................................................................ 23 4.5.2 Quantifying errors and uncertainty ................................................................................... 25 5 Augmenting Twitter Sentiment Maps with Social Media Framework Maps......................... 27 6 Conclusion ..................................................................................................................................... 29 Bibliography .......................................................................................................................................... 32 Appendix A: Social Media Use (Twitter) Framework Technical Documentation .......................... 36 Report Documentation Page (SF 298) .............................................................................................. 88-
dc.format.extent96 pages / 14.62 Mb-
dc.format.mediumPDF/A-
dc.language.isoen_USen_US
dc.publisherConstruction Engineering Research Laboratory (U.S.)en_US
dc.publisherEngineer Research and Development Center (U.S.)-
dc.relation.ispartofseriesTechnical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC/CERL TR-19-14-
dc.rightsApproved for Public Release; Distribution is Unlimited-
dc.sourceThis Digital Resource was created in Microsoft Word and Adobe Acrobat-
dc.subjectSocial mediaen_US
dc.subjectSocial media--Analysisen_US
dc.subjectTwitteren_US
dc.subjectPopulation--Analysisen_US
dc.subjectMilitary operationsen_US
dc.titleQuantifying uncertainty in population weighting of Twitter analyses for urban risk assessmenten_US
dc.typeReporten_US
Appears in Collections:Technical Report

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